Existing neural approaches typically evaluated on single-intent utterance, however in a real-world scenario users may indicate multiple-intent in an utterance e.g. "Show me all flights from Atlanta to London and get the cost" [1]} or even expressing multiple sentences in one single turn. While most datasets for slot filling and intent classification are single-turn utterance, there are some recent multi-turn datasets that provide slot annotation on the token-level, namely the Restaurant-8K, TaskMaster-1 and 2 [2]}, and Frame [3]} datasets. The subset of Schema Guided Dialogue (SGD) dataset [4]} used in DTSC-8 is also annotated with slots in the token-level and covers 16 domains. In addition to that, the TOP dataset [5]} introduces datasets annotated with hierarchical representation and MTOP dataset [6]} provides both flat and hierarchical representation on 6 languages across 11 domains.
